def test_median():
# simple tests
x = np.arange(1, 11)
m = pra.median(x)
assert m == 5.5
x = np.arange(1, 12)
m = pra.median(x)
assert m == 6
# test dimensions
x = np.random.rand(10, 9, 8)
m, ci = pra.median(x, alpha=0.05)
assert m.shape == (10, 9)
assert ci.shape == (2, 10, 9)
m, ci = pra.median(x, alpha=0.05, axis=1)
assert m.shape == (10, 8)
assert ci.shape == (2, 10, 8)
m, ci = pra.median(x, alpha=0.05, axis=1, keepdims=True)
assert m.shape == (10, 1, 8)
assert ci.shape == (2, 10, 1, 8)
# Now test statistical property of the confidence interval
np.random.seed(0) # fix this for repeatable outcome
R = 200
N = [10, 100, 1000]
alpha = [0.05, 0.01]
from scipy.stats import uniform
dist = uniform()
true_median = dist.median()
for n in N:
for a in alpha:
failures = np.zeros(R, dtype=bool)
for r in range(R):
x = dist.rvs(size=n)
m, ci = pra.median(x, alpha=a)
if true_median < m + ci[0] or m + ci[1] < true_median:
failures[r] = 1
assert sum(
failures) / R <= 1.2 * a # allow some slack for realizations
def nice_plot(x, ylabel, bf_order=None):
'''
Define a function to plot consistently the data
'''
if bf_order is None:
bf_order = beamformer_names
ax1 = plt.gca()
newmap = plt.get_cmap('gist_heat')
from itertools import cycle
# totally a hack to get the same line styles as Fig6/7
lines = ['-D','-v','->','-s','-o']
linecycler = cycle(lines)
# totally a hack to get the same line styles as Fig6/7
map1 = [newmap( k ) for k in np.linspace(0.25,0.9,5)]
map2 = [map1[3],map1[2],map1[4],map1[0],map1[1]]
ax1.set_color_cycle(map2)
# no clipping of the beautiful markers
plt.setp(ax1,'clip_on',False)
for bf in bf_order:
i = bf_dict[bf]
med, ci = pra.median(x, axis=0)
p, = plt.plot(range(0, max_sources),
#np.median(x[:,i,:], axis=0),
med[i,:],
next(linecycler),
linewidth=1,
markersize=4,
markeredgewidth=.5,
clip_on=False)
# confidence interval for the median
plt.fill_between(range(0, max_sources),
med[i,:]+ci[0,i,:], med[i,:]+ci[1,i,:],
#color='grey',
color=p.get_color(),
linewidth=0.05,
edgecolor='k',
alpha=0.3)
# Hide right and top axes
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['bottom'].set_position(('outward', 10))
ax1.spines['left'].set_position(('outward', 15))
ax1.yaxis.set_ticks_position('left')
ax1.xaxis.set_ticks_position('bottom')
# Make ticks nicer
ax1.xaxis.set_tick_params(width=.3, length=3)
ax1.yaxis.set_tick_params(width=.3, length=3)
# Make axis lines thinner
for axis in ['bottom','left']:
ax1.spines[axis].set_linewidth(0.3)
# Set ticks fontsize
plt.xticks(size=9)
plt.yticks(size=9)
# Set labels
plt.xlabel(r'Number of images $K$', fontsize=10)
plt.ylabel(ylabel, fontsize=10)
plt.legend(bf_order, fontsize=7, loc='upper left', frameon=False, labelspacing=0)
plt.xticks(size=9)
plt.yticks(size=9)
# Set labels
plt.xlabel(r'Number of images $K$', fontsize=10)
plt.ylabel(ylabel, fontsize=10)
plt.legend(bf_order, fontsize=7, loc='upper left', frameon=False, labelspacing=0)
# Here we plot the figure used in the paper (Fig. 10)
plt.figure(figsize=(4,3))
nice_plot(opesq_bf[:,0,:,:], 'PESQ [MOS]',
bf_order=['Rake Perceptual','Rake MVDR'])
# plot input SNR
med, ci = pra.median(ipesq[:,0])
o = np.ones(max_sources)
p, = plt.plot(np.arange(max_sources), np.median(ipesq[:,0])*o)
plt.text(5.0, 1.28, 'Input PESQ', fontsize=7)
'''
# confidence interval for the median
plt.fill_between(range(0, max_sources),
(med+ci[0])*o, (med+ci[1])*o,
#color='grey',
color=p.get_color(),
linewidth=0.05,
edgecolor='k',
alpha=0.3)
'''
def nice_plot(x, ylabel, bf_order=None):
'''
Define a function to plot consistently the data
'''
if bf_order is None:
bf_order = beamformer_names
ax1 = plt.gca()
newmap = plt.get_cmap('gist_heat')
from itertools import cycle
# totally a hack to get the same line styles as Fig6/7
lines = ['-D', '-v', '->', '-s', '-o']
linecycler = cycle(lines)
# totally a hack to get the same line styles as Fig6/7
map1 = [newmap(k) for k in np.linspace(0.25, 0.9, 5)]
map2 = [map1[3], map1[2], map1[4], map1[0], map1[1]]
ax1.set_color_cycle(map2)
# no clipping of the beautiful markers
plt.setp(ax1, 'clip_on', False)
for bf in bf_order:
i = bf_dict[bf]
med, ci = pra.median(x, axis=0)
p, = plt.plot(
range(0, max_sources),
#np.median(x[:,i,:], axis=0),
med[i, :],
next(linecycler),
linewidth=1,
markersize=4,
markeredgewidth=.5,
clip_on=False)
# confidence interval for the median
plt.fill_between(
range(0, max_sources),
med[i, :] + ci[0, i, :],
med[i, :] + ci[1, i, :],
#color='grey',
color=p.get_color(),
linewidth=0.05,
edgecolor='k',
alpha=0.3)
# Hide right and top axes
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['bottom'].set_position(('outward', 10))
ax1.spines['left'].set_position(('outward', 15))
ax1.yaxis.set_ticks_position('left')
ax1.xaxis.set_ticks_position('bottom')
# Make ticks nicer
ax1.xaxis.set_tick_params(width=.3, length=3)
ax1.yaxis.set_tick_params(width=.3, length=3)
# Make axis lines thinner
for axis in ['bottom', 'left']:
ax1.spines[axis].set_linewidth(0.3)
# Set ticks fontsize
plt.xticks(size=9)
plt.yticks(size=9)
# Set labels
plt.xlabel(r'Number of images $K$', fontsize=10)
plt.ylabel(ylabel, fontsize=10)
plt.legend(bf_order,
fontsize=7,
loc='upper left',
frameon=False,
labelspacing=0)
plt.legend(bf_order,
fontsize=7,
loc='upper left',
frameon=False,
labelspacing=0)
# Here we plot the figure used in the paper (Fig. 10)
plt.figure(figsize=(4, 3))
nice_plot(opesq_bf[:, 0, :, :],
'PESQ [MOS]',
bf_order=['Rake Perceptual', 'Rake MVDR'])
# plot input SNR
med, ci = pra.median(ipesq[:, 0])
o = np.ones(max_sources)
p, = plt.plot(np.arange(max_sources), np.median(ipesq[:, 0]) * o)
plt.text(5.0, 1.28, 'Input PESQ', fontsize=7)
'''
# confidence interval for the median
plt.fill_between(range(0, max_sources),
(med+ci[0])*o, (med+ci[1])*o,
#color='grey',
color=p.get_color(),
linewidth=0.05,
edgecolor='k',
alpha=0.3)
'''
def med_ci(slice_values):
''' This is used to compute median and confidence interval in dataframe '''
m, ci = median(np.array(slice_values['value']).flatten(), alpha=0.05)
return pd.Series([m, ci[0], ci[1]], index=['value', 'ci_lo', 'ci_hi'])
